In machine learning and statistics, classification is a problem of identifying to which of a set of categories a new observation belongs, on the basis of a training set of data containing observations. For example, inventories of items are often classified into categories for the convenience of users who browse these inventories. Inventory providers often organize items according to a classification scheme (e.g., a browse tree) that indicates a relationship between item categories (e.g., classifications) and items in inventory. In some cases, an item may be classified as belonging to multiple categories. Misclassified items can produce negative consequences. Continuing with the inventory example, misclassifications can negatively impact a user's searching experience. However, identifying misclassifications is not a trivial task. Providers often have to train workers to manually classify items. Training these workers can be costly and time-consuming. Even experienced classifiers can provide low through-put depending on the number of experienced classifiers and the size and complexity of the catalog being classified. Due to the high costs associated with trained workers who classify and maintain the classifications (e.g., in an electronic catalog), providers (e.g., electronic marketplace providers) may turn to crowdsourcing tasks to aid them in identifying misclassifications. While classifications provided by crowdsource users are certainly less expensive, it is often the case that they are also less accurate. Classifications utilizing crowdsourcing also tends to have low-throughput. Conventional techniques can make it costly to identify misclassifications in a wide variety of contexts.